Geographic Boudary

Block Group in Three Counties

# tri_cou <- c(Multnomah = "41051", Washington = "41067", Clackamas = "41005") #, Yamhill = "41071" , Clark = "53011"
tri_cou <- c('051','067','005')# "Multnomah", "Washington", "Clackamas"
pdx_bg <- block_groups(state = "41",county = tri_cou, cb = FALSE, year = 2019)
# pdx_bg <- pdx_bg %>% filter(str_sub(GEOID10, 1, 5)  %in% tri_cou)
dim(pdx_bg)
## [1] 1041   13

Vehicle Available by Workers

  • SEX OF WORKERS BY VEHICLES AVAILABLE (No Block Group data)

  • MEANS OF TRANSPORTATION TO WORK BY VEHICLES AVAILABLE (No Block Group data)

vars %>% filter(name %in% c("B08014_002","B08141_002")) %>% kbl() %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), font_size = 7)
name label concept
B08014_002 Estimate!!Total:!!No vehicle available SEX OF WORKERS BY VEHICLES AVAILABLE
B08141_002 Estimate!!Total:!!No vehicle available MEANS OF TRANSPORTATION TO WORK BY VEHICLES AVAILABLE 
get_acs(
  state = "41",
  county = tri_cou,
  geography = "block group",
  variables = c("B08014_002","B08141_002"), output = "wide", 
  cache_table = T
  ) %>% 
  select(GEOID,B08014_002E,B08141_002E) %>% str()
## tibble [1,041 × 3] (S3: tbl_df/tbl/data.frame)
##  $ GEOID      : chr [1:1041] "410050222012" "410050222011" "410050235002" "410050235005" ...
##  $ B08014_002E: num [1:1041] NA NA NA NA NA NA NA NA NA NA ...
##  $ B08141_002E: num [1:1041] NA NA NA NA NA NA NA NA NA NA ...

Vehicle Available by Household

  • HOUSEHOLD SIZE BY VEHICLES AVAILABLE (No Block Group data)

  • NUMBER OF WORKERS IN HOUSEHOLD BY VEHICLES AVAILABLE (No Block Group data)

vars %>% filter(name %in% c("B08201_002","B08203_002")) %>% kbl() %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), font_size = 7)
name label concept
B08201_002 Estimate!!Total:!!No vehicle available HOUSEHOLD SIZE BY VEHICLES AVAILABLE
B08203_002 Estimate!!Total:!!No vehicle available NUMBER OF WORKERS IN HOUSEHOLD BY VEHICLES AVAILABLE 
get_acs(
  state = "41",
  county = tri_cou,
  geography = "block group",
  variables = c("B08201_002","B08203_002"), output = "wide", # same to "B08141_002"
  cache_table = T
  ) %>% 
  select(GEOID,B08201_002E, B08203_002E) %>% str()
## tibble [1,041 × 3] (S3: tbl_df/tbl/data.frame)
##  $ GEOID      : chr [1:1041] "410050222012" "410050222011" "410050235002" "410050235005" ...
##  $ B08201_002E: num [1:1041] NA NA NA NA NA NA NA NA NA NA ...
##  $ B08203_002E: num [1:1041] NA NA NA NA NA NA NA NA NA NA ...
  • TENURE BY VEHICLES AVAILABLE
vars %>% filter(name %in% c("B25044_003","B25044_010")) %>% kbl() %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), font_size = 7)
name label concept
B25044_003 Estimate!!Total:!!Owner occupied:!!No vehicle available TENURE BY VEHICLES AVAILABLE
B25044_010 Estimate!!Total:!!Renter occupied:!!No vehicle available TENURE BY VEHICLES AVAILABLE 
(noveh <- get_acs(
  state = "41",
  county = tri_cou,
  geography = "block group",
  variables = c("B25044_003","B25044_010"), output = "wide",
  summary_var = "B25044_001", cache_table = T
  ) %>% arrange(GEOID) %>% 
  mutate(noveh = B25044_003E + B25044_010E,
         noveh.per = 100 * noveh /summary_est) %>% 
  select(GEOID,B25044_003E,B25044_010E,noveh,summary_est,noveh.per)) %>% head(20)
## # A tibble: 20 x 6
##    GEOID        B25044_003E B25044_010E noveh summary_est noveh.per
##    <chr>              <dbl>       <dbl> <dbl>       <dbl>     <dbl>
##  1 410050201001          23           0    23         922     2.49 
##  2 410050201002           8           7    15         369     4.07 
##  3 410050201003           0          64    64         405    15.8  
##  4 410050202001           0           0     0         579     0    
##  5 410050202002           0           0     0         508     0    
##  6 410050202003          16          51    67         375    17.9  
##  7 410050202004           0          16    16         878     1.82 
##  8 410050202005          16          28    44         574     7.67 
##  9 410050203021           0          53    53         536     9.89 
## 10 410050203022           8          22    30        1177     2.55 
## 11 410050203031          18           0    18        1038     1.73 
## 12 410050203032           0          70    70         564    12.4  
## 13 410050203033           0          81    81        1148     7.06 
## 14 410050203041          19           0    19         711     2.67 
## 15 410050203042           0           0     0         574     0    
## 16 410050203043          17           0    17         938     1.81 
## 17 410050204011           0           0     0         521     0    
## 18 410050204012           0          14    14         364     3.85 
## 19 410050204013           0           0     0         613     0    
## 20 410050204014           1           2     3         721     0.416

B08201_002 = B08203_002 = B25044_003 + B25044_010

Spatial Distributions

pdx_bg_noveh <- pdx_bg %>% left_join(noveh,by="GEOID")
mapview(pdx_bg_noveh, zcol = "noveh.per") + 
mapview(pdx_bg_noveh, zcol = "noveh")